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A region’s transforming care partnership identified that autistic adults without an intellectual disability (ID) may be falling through gaps in services when presenting with a significant emotional and/or behavioural need in the absence of a mental health diagnosis. The region’s intensive support teams (ISTs) for adults with ID therefore piloted a short-term “behavioural support service” for this population. The purpose of this paper is to evaluate this pilot.

This study represents a mixed-methods service evaluation over a four year pilot period. The quantitative component examined referral rates and demographic data of accepted and declined referrals; and length of referral episodes and Health of The Nation Outcomes Scores (HoNOS) for accepted referrals. The qualitative component used thematic analysis to identify key themes relating to reasons for referral, clinical/therapeutic needs, and the models of support that most informed assessments and interventions at individual and systems levels.

The ISTs accepted 30 referrals and declined 53. Most accepted referrals were male (83%), and under 24 years old (57%). Average HoNOS scores were above the thresholds generally associated with hospital admission. Key qualitative themes were: transitional support; sexual risks/vulnerabilities; physical aggression; domestic violence; and attachment, trauma and personality difficulties. Support mostly followed psychotherapeutic modalities couched in trauma, attachment and second- and third-wave cognitive behavioural therapies. Positive Behaviour Support (PBS) did not emerge as a model of preference for service users or professionals.

This project represents one of the first of this type for autistic adults without an ID in the UK. It provides recommendations for future service development and research, with implications for Transforming Care policy and guidance.

Cryptocurrency arose, and grew in popularity, following the financial crisis of 2008 built upon a promise of decentralizing money and payments. An examination of the history of money and banking in the United States demonstrates that stable money benefits from strict controls and commitments by a centralized government through chartering restrictions and a broad safety net, rather than decentralization. In addition, financial crises happen when the government allows money creation to occur outside of official channels. The US central bank is then forced into a policy of supporting a range of money-like assets in order to maintain a grip on monetary policy and some semblance of financial stability.

In addition, this chapter argues that cryptocurrency as a form of shadow money shares many of the problematic attributes of both the privately issued bank notes that created instability during the “free banking” era and the “shadow banking” activities that contributed to the 2008 crisis. In this sense, rather than being a novel and disruptive idea, cryptocurrency replicates many of the systemically destabilizing aspects of privately issued money and money-like instruments.

This chapter proposes that, rather than allowing a new, digital “free banking” era to emerge, there are better alternatives. Specifically, it argues that the Federal Reserve (Fed) should use its tools to improve public payment systems, enact robust utility-like regulations for private digital currencies and limit the likelihood of bubbles using prudential measures.

The objective of this chapter is to discuss how different techniques in Regional Science and Peace Science and the emerging techniques in Management Science can be used in analysing Disaster Management and Global pandemic with special reference to developing countries. It is necessary for me to first discuss the subjects of Disaster Management, Regional Science, Peace Science and Management Science. The objective of this chapter is to emphasise that the studies of Disaster Management should be more integrated with socioeconomic and geographical factors. The greatest disaster facing the world is the possibility of war, particularly nuclear war, and the preparation of the means of destruction through military spending.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Since the start of the COVID-19 pandemic, one dark social-media phenomenon in particular has experienced a significant rise: consumer-to-consumer (C2C) conflicts, i.e. consumers who verbally attack each other in response to COVID-19 service failures. The aim of this paper is to uncover the sources of such conflicts and to gain an insight into the corresponding conflict moderation strategies that international brands adopt.

The methodology consists of non-participatory netnographic observations of 13 national, international, and global online brand communities (OBCs) on Facebook. The authors use purposeful sampling to collect relevant data on conflict sources and brand moderation strategies during COVID-19 service failures and a hybrid approach to thematic analysis to derive distinct themes from these data.

The paper identifies five C2C conflict sources: brand attack, brand dissatisfaction, brand skepticism, brand contention and brand defense; these are then classified as having either an individualistic (self-oriented) or collectivistic (other-oriented) orientation. The authors also uncover several moderation strategies: non-engaging, automated, bolstering, asserting (direct, indirect) and informing (factual, empathetic, apologetic), which are broadly categorized into two levels based on their passive vs active approach and authoritative vs cooperative orientation. The paper further highlights that brands adapt their moderation strategies to specific sources of C2C conflicts, thereby producing a range of OBC outcomes.

The study's empirically informed framework comprising sources of undesirable conflicts and brand moderation strategies offers a practical tool that can aid marketing managers in nurturing civil C2C engagement and interactive behaviors in their OBCs. By adopting our framework, brand and marketing practitioners can tailor their communication strategies toward different sources of C2C conflict and minimize their adverse consequences, thus, fostering an overall constructive OBC engagement.

The authors offer a novel framework to international marketing research, consisting of C2C conflict sources and corresponding moderation strategies that take place in response to service failures during the COVID-19 pandemic. These insights, in turn, inform international marketers about new ways of transforming the dark side of OBCs into a source of competitive advantage based on real-world brand practice.

The purpose of this study is to examine the potential of integrating material from the arts into postgraduate curricula to deepen students’ engagement with marketing phenomena. The authors assess the use of arts-based activities, within a broader critical pedagogy, for encouraging imaginative and analytical thinking.

The authors devised two learning activities and an interpretive method for studying their value. The activities were an individual essay connecting themes in song lyrics to marketing, and a group photography project. These were applied, within a broader, critical approach, in postgraduate modules on sustainability, ethics and critical marketing. Data collection comprised diaries kept by the teachers, open-ended feedback from students and students’ assignments.

Students showed high levels of engagement, reflexivity and depth of thought, in felt experiences of learning. Their ability to make connections not explicitly in the materials, and requiring imaginative jumps, was notable. Several reported lasting changes to their behaviour. Some found the tasks initially intimidating or, once they were more engaged, stressful or saddening.

This adds to scholarship on management education by showing the usefulness of an arts-based approach towards a transformative agenda.

It offers a template of how to draw from the arts to strengthen critical engagement upon which marketing teachers can build. It also contains practical advice on the challenges and benefits of doing so.

The authors provide evidence that this approach can enhance sensitivity and reflexivity in students, potentially producing more ethical and sustainable decisions in future.

The pedagogical interventions are novel and of value to lecturers seeking to enhance critical engagement with theory. An empirical study of an attempt to integrate arts into teaching marketing represents a promising direction, given the discipline’s creative nature.

The study aims to investigate the impact of workplace ostracism (WO) and fear of the COVID-19 pandemic on the family life of restaurant employees. This research is based on the conservation of resources (COR) theory and work–family interface model to understand the theoretical underpinnings of mistreatment in the food sector during the COVID-19 pandemic.

The study utilized a survey with a structured questionnaire to collect time-lagged data from 238 restaurant employees in the central region of Punjab province in Pakistan. The collected data were analyzed using the SPSS tool with modern-day techniques like bootstrapping, process macro and SmartPLS.

The study reveals that perceived stress levels of the employees increase due to ostracism, leading to work–family conflict. Furthermore, the study found that employees who fear COVID-19 are less stressed by ostracism.

The study's significant contribution lies in demonstrating that the impact of ostracism in the workplace is quite different from what was expected. The results have shown that ostracism can reduce the perceived stress levels of employees, leading to a decrease in work–family conflict, especially in the presence of fear of COVID-19.

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.